LLMs as survey respondents: Comparison to simple semantic baselines
Large language models (LLMs) have been explored as surrogates for human survey respondents.
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Key points
- A more promising application is data augmentation, where LLMs predict new responses from partial human data.
- However, the semantic similarity between questionnaire items is a known source of correlation between answers.
- To assess this, we evaluated how three LLMs can predict responses to held-out items in two surveys measuring attitudes toward automated or light-electric vehicles, based on varying proportions of known responses.
- Our results show that LLMs' capacities for augmenting replies varied across surveys, and can revert to the same levels as simpler embedding similarity baselines when less data is available.
Sources (1)
- [1]LLMs as survey respondents: Comparison to simple semantic baselinesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:26 PM
Large language models (LLMs) have been explored as surrogates for human survey respondents.
A more promising application is data augmentation, where LLMs predict new responses from partial human data.
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